CompactRegressionGAM
Description
CompactRegressionGAM
is a compact version of a RegressionGAM
model
object (GAM for regression). The compact model does not include the data used for training the
model. Therefore, you cannot perform some tasks, such as cross-validation, using the compact
model. Use a compact model for tasks such as predicting the responses of new
data.
Creation
Create a CompactRegressionGAM
object from a full RegressionGAM
model
object by using compact
.
Properties
GAM Properties
Interactions
— Interaction term indices
two-column matrix of positive integers | []
This property is read-only.
Interaction term indices, specified as a t
-by-2 matrix of positive
integers, where t
is the number of interaction terms in the model.
Each row of the matrix represents one interaction term and contains the column indexes
of the predictor data X
for the interaction term. If the model does
not include an interaction term, then this property is empty
([]
).
The software adds interaction terms to the model in the order of importance based on the p-values. Use this property to check the order of the interaction terms added to the model.
Data Types: double
Intercept
— Intercept term of model
numeric scalar
This property is read-only.
Intercept (constant) term of the model, which is the sum of the intercept terms in the predictor trees and interaction trees, specified as a numeric scalar.
Data Types: single
| double
IsStandardDeviationFit
— Flag indicating whether standard deviation model is fit
false
| true
Flag indicating whether a model for the standard deviation of the response
variable is fit, specified as false
or true
.
Specify the 'FitStandardDeviation'
name-value argument of
fitrgam
as true
to fit the model for the
standard deviation.
If IsStandardDeviationFit
is true
, then
you can evaluate the standard deviation at a new observation by using predict
.
This function also returns the prediction intervals of the response variable,
evaluated at given observations.
Data Types: logical
Other Regression Properties
CategoricalPredictors
— Categorical predictor indices
vector of positive integers | []
This property is read-only.
Categorical predictor
indices, specified as a vector of positive integers. CategoricalPredictors
contains index values indicating that the corresponding predictors are categorical. The index
values are between 1 and p
, where p
is the number of
predictors used to train the model. If none of the predictors are categorical, then this
property is empty ([]
).
Data Types: double
ExpandedPredictorNames
— Expanded predictor names
cell array of character vectors
This property is read-only.
Expanded predictor names, specified as a cell array of character vectors.
ExpandedPredictorNames
is the same as PredictorNames
for a generalized additive model.
Data Types: cell
PredictorNames
— Predictor variable names
cell array of character vectors
This property is read-only.
Predictor variable names, specified as a cell array of character vectors. The order of the
elements in PredictorNames
corresponds to the order in which the
predictor names appear in the training data.
Data Types: cell
ResponseName
— Response variable name
character vector
This property is read-only.
Response variable name, specified as a character vector.
Data Types: char
ResponseTransform
— Response transformation function
'none'
| function handle
Response transformation function, specified as 'none'
or a function handle.
ResponseTransform
describes how the software transforms raw
response values.
For a MATLAB® function or a function that you define, enter its function handle. For
example, you can enter Mdl.ResponseTransform =
@function
, where
function
accepts a numeric vector of the
original responses and returns a numeric vector of the same size containing the
transformed responses.
Data Types: char
| function_handle
Object Functions
Interpret Prediction
lime | Local interpretable model-agnostic explanations (LIME) |
partialDependence | Compute partial dependence |
plotLocalEffects | Plot local effects of terms in generalized additive model (GAM) |
plotPartialDependence | Create partial dependence plot (PDP) and individual conditional expectation (ICE) plots |
shapley | Shapley values |
Examples
Reduce Size of Generalized Additive Model
Reduce the size of a full generalized additive model (GAM) for regression by removing the training data. Full models hold the training data. You can use a compact model to improve memory efficiency.
Load the carbig
data set.
load carbig
Specify Acceleration
, Displacement
, Horsepower
, and Weight
as the predictor variables (X
) and MPG
as the response variable (Y
).
X = [Acceleration,Displacement,Horsepower,Weight]; Y = MPG;
Train a GAM using X
and Y
.
Mdl = fitrgam(X,Y)
Mdl = RegressionGAM ResponseName: 'Y' CategoricalPredictors: [] ResponseTransform: 'none' Intercept: 26.9442 IsStandardDeviationFit: 0 NumObservations: 398
Mdl
is a RegressionGAM
model object.
Reduce the size of the model.
CMdl = compact(Mdl)
CMdl = CompactRegressionGAM ResponseName: 'Y' CategoricalPredictors: [] ResponseTransform: 'none' Intercept: 26.9442 IsStandardDeviationFit: 0
CMdl
is a CompactRegressionGAM
model object.
Display the amount of memory used by each regression model.
whos('Mdl','CMdl')
Name Size Bytes Class Attributes CMdl 1x1 597222 classreg.learning.regr.CompactRegressionGAM Mdl 1x1 631046 RegressionGAM
The full model (Mdl
) is larger than the compact model (CMdl
).
To efficiently predict responses for new observations, you can remove Mdl
from the MATLAB® Workspace, and then pass CMdl
and new predictor values to predict
.
Version History
Introduced in R2021a
Comando de MATLAB
Ha hecho clic en un enlace que corresponde a este comando de MATLAB:
Ejecute el comando introduciéndolo en la ventana de comandos de MATLAB. Los navegadores web no admiten comandos de MATLAB.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)